Optimized metrics for orthogonal combinatorial CRISPR screens

CRISPR-based gene perturbation enables unbiased investigations of single and combinatorial genotype-to-phenotype associations. In light of efforts to map combinatorial gene dependencies at scale, choosing an efficient and robust CRISPR-associated (Cas) nuclease is of utmost importance. Even though SpCas9 and AsCas12a are widely used for single, combinatorial, and orthogonal screenings, side-by-side comparisons remain sparse. Here, we systematically compared combinatorial SpCas9, AsCas12a, and CHyMErA in hTERT-immortalized retinal pigment epithelial cells and extracted performance-critical parameters for combinatorial and orthogonal CRISPR screens. Our analyses identified SpCas9 to be superior to enhanced and optimized AsCas12a, with CHyMErA being largely inactive in the tested conditions. Since AsCas12a contains RNA processing activity, we used arrayed dual-gRNAs to improve AsCas12a and CHyMErA applications. While this negatively influenced the effect size range of combinatorial AsCas12a applications, it enhanced the performance of CHyMErA. This improved performance, however, was limited to AsCas12a dual-gRNAs, as SpCas9 gRNAs remained largely inactive. To avoid the use of hybrid gRNAs for orthogonal applications, we engineered the multiplex SpCas9-enAsCas12a approach (multiSPAS) that avoids RNA processing for efficient orthogonal gene editing.

. SpCas9 performs better than enAsCas12a in single and combinatorial CRISPR screens. (a) Schematic representation of SpCas9 and enAsCas12a nuclease expression constructs and SpCas9, enAsCas12a, CHyMErA gRNA expression constructs. (b) Core essential (CE) and tumor suppressor (TS) genes multiplex library design. Combinatorial gRNAs targeting 10 CE and 10 TS genes with 25,600 gRNA combinations; 4 gRNA for each gene together with 80 non-human targeting (NHT) control gRNAs in each cassette. (c) CE/TS library screening setup. RPE1 cells were screened with 100-fold library coverage for all three libraries in duplicates. Cells were transduced with an MOI of 0.5. gDNA was harvested on day 14 and prepared for sequencing. End time point and library read counts were used to compute log 2   www.nature.com/scientificreports/ AAVS1 and TP53 genes with SpCas9 or enAsCas12a. Again, this identified SpCas9 to be superior to enAsCas12a, an effect that was independent of the number of used gRNAs ( Supplementary Fig. S4b). However, a larger effect size range does not mean that the identified phenotypes differ. We, therefore, compared gene-level LFCs across screens which revealed an overall high concordance among them (SpCas9-enAs-Cas12a: r = 0.88, SpCas9-CHyMErA: r = 0.84, enAsCas12a-CHyMErA: r = 0.87) (Fig. 1i-k), which is consistent with our previous AU-ROC analysis ( Fig. 1e and Supplementary Fig. S1d), suggesting effect size range and hit ranks being uncoupled, at least for essential and tumor suppressor phenotypes. Together, these results identify SpCas9 to work more robustly than enAsCas12a and CHyMErA in combinatorial CRISPR screens.
Hybrid guide RNAs decrease effect size range in CHyMErA applications. The CHyMErA platform has been introduced as a combinatorial orthogonal gene editing approach. Here, a hybrid RNA containing a Cas9 and a Cas12a gRNA is expressed from a single U6 promoter. CHyMErA Cas9 and Cas12a gRNAs are designed by established design rules and the CHyMErA-Net algorithm, respectively, collectively resulting in higher gene depletion than both single Cas9 and Cas12a gRNAs alone 9,25 .
Our analysis revealed CHyMErA to greatly underperform compared to SpCas9 and enAsCas12a, not only in effect size range but also in speed of phenotype induction (Figs. 1d,e and 2g). Since CHyMErA gRNAs of the CE/ TS library were identical to the gRNAs of the combinatorial SpCas9 and enAsCas12a libraries, we computationally separated CHyMErA hybrid gRNAs into single-transcript SpCas9 or enAsCas12a sgRNAs and compared their performance in combinatorial and orthogonal conditions. Only little concordance could be observed among SpCas9 and enAsCas12a gRNAs within CHyMErA, but also in between CHyMErA and SpCas9 or enAsCas12a libraries (CHyMErA (SpCas9 vs. enAsCas12a): r = 0.3, SpCas9 vs. CHyMErA (SpCas9): r = 0.51, enAsCas12a vs. CHyMErA (enAsCas12a): r = 0.48) (Fig. 2a-c), an effect that only slightly improved when aggregating individual gRNAs to genes ( Fig. 2d-f). Ensuring that the observed effect was independent of plasmid design and choice of RNA promoter, we targeted the AAVS1 locus with a single gRNA and determined the editing efficiencies of each plasmid by TIDE 26 . This revealed high and comparable editing rates (> 85%), regardless from which cassette the gRNA was expressed (h7SK or hU6). However, we noticed that the SpCas9 gRNA within the CHyMErA plasmid had lower editing rates (52.7%) when compared to its independent expression (92.2%), as well as the Cas12a gRNA of the CHyMErA plasmid (91.4%) ( Supplementary Fig. S10b). CHyMErA gRNAs differ from SpCas9 and enAsCas12a gRNAs in their structure and need for RNA processing 9,27 , we asked if RNA processing, and indirectly their structure, was preventing CHyMErA gRNAs to perform as their unprocessed counterparts. To answer this question, we transduced RPE1 cells with paired gRNA constructs, targeting TP53 and HPRT1, that either required processing (dual-gRNAs) or as single-transcript sgRNAs (sgRNAs). Even in DMSO-treated cells, sgRNAs induced strong and significant cell proliferation, whereas dual-gRNAs were indistinguishable from NHT control guides (Fig. 2g). In line with this, Nutlin-3 and 6TG exposure prevented NHT-transduced cells from proliferating and induced a significantly earlier proliferation of cells transduced with sgRNAs over dual-gRNAs (Fig. 2g). We then performed Northern Blotting to quantify RNA processing of gRNAs derived from the CHyMErA plasmid. This revealed a comparable expression of RNA transcripts and the identification of an unexpected 139 nts-long sequence that could not be detected in the SpCas9 or enAsCas12a samples. This RNA fragment matched the size of the unprocessed SpCas9-enAsCas12a hybrid RNA transcript ( Supplementary  Fig. S10a). This suggests that, RNA processing is likely not the major cause of the observed differences. However, due to possible improper hybrid RNA folding of CHyMErA gRNAs, it may still account for the slow induction and, ultimately, inefficient separation of gRNA-associated phenotypes.
Single gene-targeting gRNA arrays compensate for enAsCas12a RNA processing. The ability of enAsCas12a to process polycistronic gRNAs is a powerful means to improve single-gene knockout rates and enable multi-gene edits 28,29 . Even though RNA processing negatively impacted phenotype induction and effect size range of CHyMErA, we asked if polycistronic gRNA expression would improve the performance of combinatorial enAsCas12a and CHyMErA approaches by targeting two different loci in the same gene with two pre-defined gRNAs. To this end, we designed two additional 3Cs library plasmids enAsCas12a(dual-gRNA) and CHyMErA.v2 (Fig. 3a), and used them to generate combinatorial CRISPR libraries targeting the same set of CE and TS genes as before ( Fig. 1b and Supplementary Fig. S5a,b). Screens were performed in the SpCas9 and enAsCas12a-expressing RPE1 cells with paired-end gRNA sequencing after 14 days of culture (MOI 0.5, 100-fold coverage, two technical replicates) ( Supplementary Fig. S6a). Again, the replicate correlation was high on gRNA and gene counts, as well as gRNA LFC-levels (gRNA: enAsCas12a(dual-gRNA) r = 0.93, CHyMErA.v2 r = 0.95; gene: enAsCas12a(dual-gRNA) r = 0.91, CHyMErA.v2 r = 0.90; gRNA-LFC: enAsCas12a(dual-gRNA) r = 0.88, CHyMErA.v2 r = 0.91) (Supplementary Fig. S6b-g). Interestingly, gRNA and gene effect size range, as well as the mean LFC of CE and TS genes derived from enAsCas12a(dual-gRNA) screens were decreased compared to single sgRNAs (Fig. 3b,c). Moreover, when analyzing the gene LFC distribution of gRNAs targeting core essential genes, the range over which phenotypes developed more than doubled from 1.43 to 3.01 (Fig. 3d). Despite these changes, gene ranks correlated well among enAsCas12a(dual-gRNA), enAsCas12a and SpCas9 (enAsCas12a(dual-gRNA) vs. enAsCas12a: r = 0.84, enAsCas12a(dual-gRNA) vs. SpCas9: r = 0.83)) ( Fig. 3d,e). Together, these data demonstrate that gRNA processing not only negatively impacts the effect size range of enAs-Cas12a CRISPR screens but also reduces the derived confidence in phenotypes.
Next, we investigated the effect of arrayed enAsCas12a gRNAs on the performance of CHyMErA.v2. In contrast to enAsCas12a(dual-gRNA), we observed a markedly improved performance when compared to the original CHyMErA ( Fig. 3b-f). The effect size range of gRNA and gene level improved almost two-fold to 11.71 and 6.20 (from 6.47 and 3.19), respectively (Fig. 3b). Moreover, mean gene LFCs of core essential genes increased by three-fold to -3.69 (from − 1.15), which resulted in a total effect size range similar to single enAsCas12a and    . 3l and Supplementary Fig. S6h). Together, these data demonstrated that enAsCas12a dual-gRNAs greatly improved the performance of CHyMErA.v2 and that SpCas9 gRNAs remained limited in their activity due to their need for RNA processing.
Robust and efficient orthogonal CRISPR screens with multiSPAS. The use of different Cas nucleases within the same cell enables orthogonal CRISPR approaches 9,10 . However, the robust performance of these approaches depends on both nucleases and their associated gRNAs performing optimally. Our data demonstrated that CHyMErA.v2 generates an improved effect size range when compared to its original version, but also that the activity of CHyMErA-intrinsic SpCas9 gRNAs is low due to RNA processing. We, therefore, engineered the orthogonal multiplex SpCas9-enAsCas12a approach (multiSPAS) in which SpCas9 and enAsCas12a gRNA-expression are decoupled by the use of two separate promoters each driving a single gRNA (Fig. 4a). We then used this plasmid for combinatorial library generation and screening, targeting the above-mentioned CE and TS genes ( (Fig. 4e). This demonstrated that the single enAsCas12a gRNAs of multiSPAS performed comparably to the arrayed enAsCas12a gRNAs of CHyMErA.v2. Next, we focused on analyzing the phenotypes of the SpCas9 gRNAs within multiSPAS, as these underperformed in previous orthogonal approaches. SpCas9 gRNAs of the multiSPAS plasmid showed a dramatic improvement in CE phenotypic separation, making them indistinguishable from the single SpCas9 library approach (mean LFC of CE genes; multiSPAS: -3.29, SpCas9: − 3.32) (Fig. 4f,g). This observation was further supported by an AU-ROC analysis comparing SpCas9, CHyMErA, CHyMErA.v2, and multiSPAS which revealed their overall similar performances in identifying CE and TS genes (AU-ROC; SpCas9: 0.84/0.96, CHyMErA: 0.82/0.92, CHyMErA.v2: 0.81/0.96 and multiSPAS: 0.82/0.94) ( Fig. 4h and Supplementary Fig. S8e). Taking the improved effect size range of multiSPAS into account, this design was identified to be the most efficient among the tested orthogonal screening approaches. These data demonstrate that, by avoiding RNA hybrids, multiSPAS enables high performance of SpCas9 and enAsCas12a gRNAs for the identification of combinatorial genotypeto-phenotype associations. Moreover, generating libraries with large combinatorial diversities benefits from the use of independent oligo-pools that are combined in vitro, avoiding cost-ineffective pools with predefined gRNA combinations. enAsCas12a has a higher effect size range than opAsCas12a in multiSPAS screens. Optimized AsCas12a (opAsCas12a) is a rationally engineered version of AsCas12a, containing six c-terminal c-myc nuclear localization signals (NLSs) and two amino acid changes in the DNA binding domain, which was shown to have high-performance genome editing when targeting conserved protein domains 30 . Thus, we asked whether opAs-Cas12a would outperform enAsCas12a in orthogonal CHyMErA screens when gRNAs are designed based on an   www.nature.com/scientificreports/ unbiased deep-learning approach. To investigate this, we used the above-described multiSPAS CRISPR library to target CE and TS genes in RPE1 cells stably expressing SpCas9 and opAsCas12a and compared the derived data to the enAsCas12a data set (Figs. 1b,c and 5a). The abundance of paired gRNAs was determined by deepsequencing after 14 days of continuous cell culture, with library coverage maintained at 100-fold ( Supplementary  Fig. S9a). The high replicate correlation was observed for gRNA and gene read counts, as well as gRNA LFC levels (gRNA r = 0.96; gene r = 0.98; gRNA-LFC r = 0.90) ( Supplementary Fig. S9b-d). When looking at gRNA and gene level effect size range first, no substantial difference between enAsCas12a and opAsCas12a could be observed (gRNAs/genes; enAsCas12a: 10.74/6.51, opAsCas12a: 10.48/6.71) (Fig. 5b). This finding was further supported by a high Spearman correlation of multiSPAS gRNA pairs (r = 0.93), as well as when comparing the activity of SpCas9 gRNAs under both conditions (SpCas9 gRNAs from enAsCas12a vs. opAsCas12a, r = 0.97) (Fig. 5c,d). However, when comparing the performance of Cas12a gRNAs among both enzymes, we observed an overall weaker performance of gRNAs when used by opAsCas12a, indicated by their relatively low correlation to enAsCas12a gRNAs (AsCas12a from enAsCas12a vs. opAsCas12a r = 0.78) (Fig. 5e). We observed comparable transcript and protein abundance of enAsCas12a and opAsCas12a ( Supplementary Fig. S7a,b), which supports the conclusion that the improved performance of enAsCas12a is not related to a difference in protein abundance but rather a consequence of different editing efficiencies. Since opAsCas12a and enAsCas12a have different PAM requirements, with TTTV PAMs being efficiently used by both 30,31 , we repeated our analysis by only considering TTTV PAM-containing gRNAs. Still, opAsCas12a underperformed with respect to enAsCas12a, demonstrated by their relatively low correlation (AsCas12a TTTV-PAM gRNAs; r = 0.72) (Fig. 5f). Together, this demonstrates www.nature.com/scientificreports/ that when AsCas12a gRNAs are not designed to target conserved protein domains, enAsCas12a displays larger effect size range in drop-out CRISPR screens.

Discussion
In the present study, we elucidated performance-critical parameters for combinatorial and orthogonal CRISPR screens. We compared combinatorial SpCas9, AsCas12a, and orthogonal CHyMErA screening approaches by targeting the same gene set under comparable experimental conditions and observed that single and paired gRNA effect size ranges are the largest with SpCas9. In line with our findings, the gRNA effect size range of AsCas12a was reported to be low 29 , and even though AsCas12a has been engineered and optimized to perform in CRISPR screens, also the recent literature indicates that AsCas12a is not as robustly performing as SpCas9 20,32 . However, the low effect size range associated with enAsCas12a and CHyMErA may be attributed to either difference in nuclease activity or the lack of respective highly-active spacer sequences. Despite SpCas9 and AsCas12a showing equivalent in vitro nuclease activity 33 , cellular applications with AsCas12a lag behind SpCas9 applications, which limits the performance of deep-learning gRNA design tools in finding highly-active AsCas12a gRNAs 14,34 . However, as more data on AsCas12a screens become available, AsCas12a gRNA design tools, and subsequently the quality of AsCas12a genetic screens, are expected to improve. We are aware that our conclusion of CHyMErA underperforming in combinatorial CRISPR screens contradicts previous observations 9 . These, however, may be explained by differences among the two used CHyMErA designs. In particular, the use of different Cas12a variants (As-Cas12a instead of Lb-Cas12a), varying numbers of SpCas9-coupled NLS sequences (this study uses 1 NLS while Gonatopoulos-Pournatzis et al. used 2 NLS), and the use of different Cas9 tracrRNAs (this study uses the wildtype sequence while Gonatopoulos-Pournatzis et al. used the engineered v2 sequence).gRNA combinations can be designed to target multiple genes at once or to target a single gene locus to increase the editing efficiency of the targeted gene. Targeting the same gene with multiple gRNAs increases the knockout efficiency, as has been shown for SpCas9 and AsCas12a applications 13,32,35 . Applying this concept, we engineered enAsCas12a(dual-gRNA) and CHyMErA.v2 library formats containing arrayed enAsCas12a gRNAs that target the same gene. Surprisingly, this strategy negatively impacted the enAsCas12a screens, while it enhanced the effect size range of CHyMErA applications. This difference may be explained two-fold: (i) the hybrid gRNA of CHyMErA folds so that the SpCas9 gRNA inhibits the first enAsCas12a gRNA, while the second enAs-Cas12a gRNA is free for binding and processing by enAsCas12a, and (ii) the different baseline performances of enAsCas12a and CHyMErA. Testing the first possibility will require additional experimental work. The second possibility is supported by the relatively high level of enAsCas12a performance (compared to SpCas9), while CHyMErA displayed only a marginal effect size range. This may be explained by the fact that the ratio of total to processed RNA in CHyMErA applications, irrespectively on the used Cas12a nuclease (Lb or As), is reported to be limited to 60% 9 . Although the performance improvements were seen with CHyMErA.v2, a closer look at individual gRNA activities revealed that SpCas9 gRNAs are mostly non-functional. This is well in line with the conclusion that the folding of hybrid gRNAs prevents the nuclease from accessing them, which also suggests that the CHyMErA.v2 phenotypes are likely driven by the second AsCas12a gRNA. Moreover, the improved effect of CHyMErA.v2 was limited to the targeting of core essential genes, which suggests the use of SpCas9 approaches when investigating positive gene fitness effects. Furthermore, even though library uniformity was previously predicted and shown to be critical for the experimental scale 13,36,37 , and since experimental parameters were kept consistent throughout our experiments, we rule out library distribution or coverage to confound the conclusion drawn here. Our experiments and analysis identified AsCas12a dual-gRNAs to vastly underperform when compared to their sgRNAs counterparts. This observation was not restrictive to single, but also prominent in orthogonal applications. This is well in line with the literature in which the design and the sequence of the direct repeat, which separates two gRNAs in AsCas12a applications, influences the efficiency of gRNA processing 14,27 . Our presented library formats contain improved direct repeat sequences, thus, the underperformance of dual-gRNAs to sgRNAs is likely independent of the used direct repeats. Additionally, the use of different direct repeat variants avoids potential lentiviral recombination issues which can affect the performance of enAsCas12a(dual-gRNA) and CHyMErA.v2 in screens. However, since the design of our direct repeat does not contain a recently described separator sequence, which was demonstrated to improve RNA processing and downstream gRNA activity 27 , our combinatorial and orthogonal library formats containing AsCas12a dual-gRNAs can likely be improved further. opAsCas12a is an engineered AsCas12a version that contains six c-terminal c-myc nuclear localization signals (NLSs) and two amino acid changes in the DNA binding domain 30 . Recently, opAsCas12a was demonstrated to have comparable performance as SpCas9 in single gene knockout screens 30 . This is in contrast to our observation of SpCas9 working better than enhanced and optimized AsCas12a in orthogonal multiSPAS screens. While the different performances cannot be explained by restricting our analysis to TTTV PAMs, it may be explained by Gier et al. using an approach in which functional protein domains are targeted by multiple gRNAs, with their averaged effect serving as a surrogate for gene loss-of-function strength 30 . This strategy is well known to improve the knockout effect size range, even for SpCas9 38 . Since gRNAs in our study were designed by CRISPick 34 , which uses a deep-learning approach based on prior knowledge of functional gRNAs, the performance of multiSPAS with optimized AsCas12a may be improved by adapting gRNA sequences to target active domains. It should, however, be noted that this strategy is limited to genes for which functional domains are known, thus interfering with genome-scale applications.
In summary, our side-by-side comparison systematically investigates the performance of SpCas9, enhanced and optimized AsCas12a, as well as CHyMErA in combinatorial and orthogonal CRISPR screens, and identifies SpCas9 to provide the most robust effect size range across the tested conditions. While dual-gRNA targeting of single genes increases the performance of CHyMErA, SpCas9 gRNAs remain inactive due to the need for RNA www.nature.com/scientificreports/ processing. Despite this, we acknowledge that increasing the sample size of this study (e.g. number of tested genes and conditions) would likely increase the differences observed between the tested combinatorial plasmid designs. Combining these findings, we engineered multiSPAS, in which SpCas9 and AsCas12a gRNAs are physically separated which results in both of them being highly active in cells. This identified multiSPAS as a robust orthogonal CRISPR approach to studying genotype-to-phenotype associations.  13,21,39 . To enable specific annealing to either h7SK and hU6 (SpCas9, enAs-Cas12a, enAsCas12a(dual-gRNA), multiSPAS) or SpCas9 and AsCas12a (CHyMErA, CHyMErA.v2) gRNA expression cassettes, the 3Cs oligonucleotides were designed with two distinct homology regions which flank the dedicated 20 (for SpCas9) or 23 (enAsCas12a) nucleotide-long gRNA sequences. All homology sequences were extended to have an annealing temperature of 52-55 °C. When selecting active gRNA sequences, the top 4 highest ranked SpCas9 and enAsCas12a knockout gRNAs from CRISPick were selected 14,25,34,40 . For the dual-gRNA approaches (enAsCas12a(dual-gRNA) and CHyMErA.v2), first and fourth-ranked gRNAs or second and third-ranked gRNAs were chosen as pairs. gRNA sequences for single and pooled applications are listed in Supplementary Table S2 and S3. www.nature.com/scientificreports/ Removing the residual template plasmid and quality control. Plasmid DNA was isolated from the overnight culture to obtain the pre-library using the GeneJet Plasmid Miniprep Kit (Thermo Fisher Scientific, K0503) according to the manufacturer's protocol. 3 µg of pre-library was digested with PacI and I-SceI (New England Biolabs, R0547S and R0694S) restriction enzymes to remove the residual template plasmid from the library which has recognition sequences in the wild-type gRNA placeholder at 37 °C overnight. Digested prelibrary was purified using the DNA Clean & Concentrator-5 kit (Zymo Research, D4014), as described in the manufacturer's protocol. The digested and purified pre-library was electroporated as described before for the electroporation of 3Cs reaction products. The next day the DNA was isolated using the QIAGEN Plasmid Maxi Kit (Qiagen, 12,163) according to the manufacturer's protocol. The purified library quality was assessed by nextgeneration sequencing.

Preparation of dU
Plasmid library high-throughput sequencing. The plasmid libraries were prepared for Illumina highthroughput sequencing using PCR under the following conditions. 100 ng of the plasmid DNA was mixed with 25 µl of NEBNext High-Fidelity 2X PCR Master Mix (New England Biolabs, M0541) and 2.5 µl each primer (10 µm), then water was added to a final volume of 50 µl. PCR was performed using the following conditions: initial denaturation at 98 °C for 3 min, 20 cycles of denaturation at 98 °C for 30 s, annealing at 68 °C for 30 s and elongation at 72 °C for 30 s, and final extension at 72 °C for 5 min. The PCR products were purified from 1% agarose gel using the GeneJet Gel Extraction Kit (Thermo Fisher Scientific, K0692) according to the manufacturer's protocol. Purified amplicons were then quantified using the Qubit fluorometer (Thermo Fisher Scientific). Gel-purified PCR products of plasmid libraries and screening samples were denatured and diluted according to Illumina guidelines and set to a final concentration of 16 pM in a total volume of 600 µl and 15% PhiX control and loaded onto a MiSeq sequencer (Illumina) according to the manufacturer's protocol. Paired-end sequencing was performed, with 75 cycles per read and 8 cycles for the index read.
Screen sample high-throughput sequencing. First, the amount of genomic DNA (gDNA) needed for adequate coverage was determined by calculating the "library complexity x experimental coverage × 6.6 pg". The PCR amplification was performed in two steps. The calculated total amount of gDNA was used in the first PCR (PCR1) as 2 µg of gDNA for each reaction which was set up in 50 µl with 25 µl of NEBNext High-Fidelity 2X PCR Master Mix, 2.5 μl of each primer (10 μm) and water. The thermal cycler parameters were set as follows: the initial denaturation at 98 °C for 5 min, 20 cycles of denaturation at 98 °C for 50 s, annealing at 68 °C for 45 s, and elongation at 72 °C for 1 min, and final extension at 72 °C for 5 min. All PCR products were then pooled and mixed in the second PCR (PCR2). 25 µl of the PCR1 product was added into the PCR2 reaction together with 50 µl of NEBNext High-Fidelity 2X PCR Master Mix, 5 μl of each primer (10 μl) which contains Illumina adaptors and index sequences, and water up to 100 µl. The PCR2 was set as follows: initial denaturation at 98 °C for 3 min, 10 cycles of denaturation at 98 °C for 50 s, annealing at 68 °C for 45 s, and elongation at 72 °C for 45 s, and final extension at 72 °C for 5 min. PCR2 product was purified from 1% agarose gel and sequenced as described for the plasmid library. Primer sequences for PCR1 and PCR2 for all library constructs are listed in Supplementary Table S4.
High-throughput sequencing data quality control, read count table generation, and analysis. Read counts of individual gRNA combinations were determined using cutadapt 2.8 and Bowtie 2.3.0 41,42 .
Reads were trimmed with cutadapt in paired-end mode to the length of the respective library sequences using the constant regions upstream of the gRNA. Cutadapt was set up with an error tolerance of 0.1 and to discard untrimmed reads. The obtained sequences were then aligned to the respective gRNA library using Bowtie2 in local mode using a seed length of 11, a seed mismatch value of 1, and the interval function 'S,1.0,0.75' . To assess the uniformity of each library, the read counts were plotted with a Lorenz curve as a cumulative distribution. According to Imkeller et al. the library skew was determined by dividing the 90th percentile by the 10th percentile of the read count distribution 36 . Obtained read counts for each individual gRNA combination were normalized to the total number of generated read counts and median read count of all NHT-NHT combinations within that sample. Read counts for each screen and library are given in Supplementary Table S5. LFC values for each gRNA combination were calculated by log 2 (screen sample normalized read count/library normalized read count). LFC values for gene-gene combination were calculated by median aggregation of every gRNA-gRNA LFCs for the related genes.
Cell culture conditions. Cell culture work was performed as described previously 21  Generation and quantification of lentiviral particles. To generate the lentiviral particles 4 × 10 6 HEK293T cells were seeded in a 10 cm dish 1 day before the transfection. On the day of transfection 2 mL Opti-MEM I (Thermo Fisher Scientific, 31985062), 210 µl of GeneJuice transfection reagent (Sigma Aldrich, 70967-4), 33 µg of the transfer vector, 27 µg of psPAX2 (Addgene: 12260) and 10 µg of pMD2.G (Addgene: 12259) were mixed. This mixture was generously vortexed and incubated for 30 min at room temperature. After incubation, the mixture was added dropwise to the cells. The supernatant was collected 48 h after the transfection and stored at -80 °C. To determine the titer of the lentiviral particles, 50,000 RPE1 cells were seeded in two 6-well plates in 2 ml of DMEM/F12 together with 8 µg/ml polybrene (Sigma-Aldrich, H9268). Cells were transduced with a ten-fold dilution series of viral supernatant (10 2 to 10 11 ). Two days after the transduction, cells were selected with puromycin (1 µg/ml, InvivoGen ant-pr-5). Established colonies were counted 10-14 days after the transduction and the colony number in the highest dilution was volume normalized to determine the lentiviral titer.
CRISPR screening. All combinatorial CRISPR libraries were screened in RPE1-SpCas9-enAsCas12a or SpCas9-opAsCas12a cell lines with 100-fold coverage, each with two technical replicates. The total cell number for each screen was calculated by multiplying library complexity and the coverage of 100. A total of 6 million cells (for SpCas9, en/opAsCas12a, CHyMErA, multiSPAS libraries) or 3 million cells (for CHyMErA.v2 library) or 1.5 million cells (for enAsCas12a(dual-gRNA) library) were transduced with a respective library with MOI of 0.5. Cells were selected with puromycin (1 µg/ml) 2 days after the transduction. Two days later, the culture medium was replaced with a fresh puromycin-containing medium, and cells were cultured in a medium with puromycin until the end of the screen. Cells were subcultured and seeded at a cell number maintaining library diversity (library complexity x screening coverage) when they reached 80% confluency. Cells were harvested after 10 cell doublings, on average 14 days, after the transduction for subsequent gDNA extraction.
DNA oligonucleotides and oligonucleotide pools. Gene blocks, oligonucleotide pools, and oligonucleotides used for 3Cs reactions, cloning, PCR, and sequencing libraries were obtained from Integrated DNA Technologies (IDT) or Merck KGaA.

RPE1 SpCas9 and enAsCas12a activity. SpCas9 and enAsCas12a activity of RPE1 cells was tested by
Nutlin-3 treatment upon TP53 knockout, coupled with viability assay. First, we generated TP53 targeting gRNA constructs for both SpCas9 and enAsCas12a. RPE1 cells were seeded in a 48-well plate (1000 cells per well) and transduced with SpCas9-TP53 or enAsCas12a-TP53 or SpCas9-AAVS1 lentiviral particles. The next day, transduced cells were selected with puromycin (1 µg/ml). Three days after the transduction, cells were treated either with DMSO or Nutlin-3 (Selleckchem, S8059) (1 µM). Cell viability was recorded with alamarBlue (BioRad, BUF012A) according to the manufacturer's protocol for 14 days. gRNA sequences for the Cas activity experiment are listed in Supplementary Table S2. enAsCas12a processing activity. SpCas9-TP53 and enAsCas12a-HPRT1 targeting gRNAs were cloned into a single gRNA expressing plasmid (lentiCRISPRv2 for SpCas9 and pRDA_052 for enAsCas12a) which does not require gRNA processing and into CHyMErA plasmid which requires gRNA processing by enAsCas12a. In a 48-well plate, 1000 RPE1 cells were seeded and transduced with lentiviral particles of dual-gRNAs, sgRNAs, or NHT gRNAs. The next day, cells were selected with puromycin (1 µg/ml). Three days after the transduction, cells were treated either with DMSO or Nutlin-3 and 6-Thioguanine (Sigma Aldrich, A4882) (1 µM and 2.5 µM respectively). Cell viability was recorded with alamarBlue (BioRad, BUF012A) according to the manufacturer's protocol for 20 days. gRNA sequences for processing activity experiment are listed in Supplementary Table S2. TIDE assay. To quantify the editing efficiency of the different gRNA expression constructs, the TIDE (Tracking of Indels by decomposition) assay was performed. The gRNAs targeting the AAVS1 locus were designed using the CRISPick 14,25,34,40 . The top-ranked gRNA for SpCas9 and enAsCas12a were selected and cloned into the h7SK and hU6 gRNA expression cassettes of every plasmid used in this study (SpCas9, enAsCas12a, CHyMErA, enAsCas12a (dual-gRNA), CHymErA.v2, multiSPAS) with 3Cs multiplex-DNA synthesis protocol which was qPCR. Cells were washed, trypsinized, and harvested at 80% confluency from a 6-well plate. RNA was extracted from the cell pellet using the RNeasy Plus Mini Kit (Qiagen, 74136) according to the manufacturer's protocol. 10 µl of the RNA was subsequently reverse transcribed into cDNA using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, 4368814) following the manufacturer's instructions. Finally, quantitative Polymerase Chain Reactions were performed in technical quadruplicates as follows: 1 µl of cDNA was added to 5 µl 2X PrimaQUANT qPCR-CYBR-Mastermix (Steinbrenner, SL-9902), 1 µl 10 µM forward and reverse primer mix and 3 µl ultrapure water. A specific pair of primers was designed for SpCas9, AsCas12a, and the GAPDH gene (Supplementary Table S2). Reactions were run on a Roche LightCycler-480-system and data was analyzed using the delta-delta-CT-method.

ROC analysis.
To compare the performance of different CRISPR screening approaches we performed area under the receiver operating characteristic curve (AU-ROC) analysis. By this, we evaluated their efficiencies in resembling fitness effects of gRNAs targeting either CE or TS genes. We used the median Chronos dependency score of RPE1 cell lines according to Cancer Dependency Map (DepMap) project to define the true positive and false positive rates. We establish a gold standard in which the positive class of CE genes (n = 6, namely CCT4, EIF3B, XPO1, IARS, EFTUD2, NARS) is represented with strong negative dependency scores that are conserved among RPE1 cell lines. Similarly, we defined the positive class of TS genes (n = 5, namely ARNT, AHR, KIRREL, TP53, NF2) with strong positive scores. The remaining genes of the library (n = 9) belong to the negative class of non-essential genes. The AU-ROC plots were generated using the pROC R package, which calculates AUC values based on the LFC of all sgRNAs targeting each of the 20 genes in the library.

Data availability
Illumina sequencing data are provided as raw read count tables in Supplementary Table S5. Additionally, the datasets generated during the current study are available in the GEO repository, via https:// www. ncbi. nlm. nih. gov/ geo/ query/ acc. cgi? acc= GSE21 5175 with private token "opqremuypputbmf ". New lentiviral 3Cs plasmids associated with this study will be made available from Addgene (IDs 189632 to 189637).